Fwd: How I Prompted My Way to Publish-Ready Content - kaustubhs@groww.in - Groww Mail

#4—Prompting is a finicky art, but a worthy investment While it would be great if the three techniques I’ve shared so far immediately transform your prompts, there's a decent chance they will fall flat for your use case. My guess is that they will even become irrelevant as models become smarter and more cost-effective over time. My point in sharing

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#3—Be methodical about the examples you provide The staple of any few-shot prompt is its examples. I had strict screening criteria for the examples that were added to my contrarian, punchy, tweet Spiral. The only tweets that were allowed were those that: did not exceed the single-tweet word count (i.e., were not a thread or long post) had over 100,

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#2—Show-based telling > tell-based telling Showing your LLM examples of what it is you’re trying to create is the principle of showing vs. telling—a key tenet of storytelling the world over (and something Geroge Lucas certainly understood). But I’ve found that showing is also an excellent way to approach any telling that you need to do. In accor

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#1—Less is often more when it comes to instructions For whatever reason, the presence of certain words can skew LLMs into unexpected behaviors. Additional instructions will often go ignored until you remove the offending words or instructions. Recent research shows that a less-is-more approach seems to work for LLMs on a pretty profound level. For

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Overcoming few-shot friction There is one problem I’ve noticed with example-based prompting: Most creators and businesses aren’t using it. I think this is because you have to overcome three major inconveniences before you start to take advantage of few-shot prompting on a consistent basis: Upfront effort: A few-shot prompt is only as good as the ex

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